OutSystems Agent Workbench
The excitement around agentic AI is intense, but so are the risks. Gartner predicts that more than 40% of agentic AI projects will fail by 2027.
How do you keep your projects from being part of that 40%? For one, you should not adopt multiple tools for each project. Instead, as you evaluate your options, you should look for a tool or platform that:
- Enhances your existing systems
- Extends the reach of your team without additional burden on IT
- Empowers you to build and govern agents that work seamlessly across the enterprise.
This article shares tips for how to avoid the most common agentic AI pitfalls and offers practical, easy-to-apply advice to help you scale AI agents and deliver measurable business outcomes.
Most agentic AI pilots fail. Here’s why
More than two-thirds of organizations say their AI initiatives remain stalled in experimentation or pilot mode, according to McKinsey’s 2025 Global State of AI Survey. IT teams won’t get unstuck unless they solve these seven unique agentic AI challenges.
Development complexity
71% of organizations rank slow, intricate builds as a dominant hurdle to AI adoption.
Agents must reason, execute tasks without intervention, trigger human-in-the-loop oversight, and adapt to changing environments. Building and maintaining custom agents across their lifecycle is no easy feat, consuming considerable time and effort. Developers also need to be able to iterate on agents without having to start over as more data is collected and engineered into their models.
Agent sprawl (too many agentic tools)
44% of software executives see increased technical debt and AI sprawl as major risks.
When an enterprise lacks a single strategy for deploying agentic AI, every department will pick their own point solutions. This leads to agent sprawl, where you have too many overlapping systems and tools, resulting in wasted money, duplicate work, and data risks. What’s more, as the number of agents scales across the enterprise, quality assurance becomes a greater challenge, making it nearly impossible to turn ideas into production-ready agents rapidly.
Equally concerning is “agent washing,” which occurs when brands purposely mislabel their solutions as “agentic.” Most of these offerings are actually complex generative AI chatbots or robotic process automation tools lacking the intelligence, adaptability, and autonomy that define true AI agents.
Data access and integration challenges
80% of businesses cite data integration as a major challenge for AI adoption.
Data quality is often cited as a barrier to agentic AI success, and the root cause is data silos and incompatible formats across enterprise systems. Consider the case of building a procurement agent. The agent needs full enterprise context to identify the supplier in the CRM, verify approval authority using HCM data, and gather payment information in the ERP. It may need additional context locked away in contracts or policy documents. Data disconnects at any point in the process can produce unreliable outputs, such as authorizing purchases that don’t align with the corporate budget.
Talent and skills gaps
63% of software executives believe AI will require significant, rapid upskilling of development teams.
Building and scaling agentic AI requires unique skills in AI architecture, evaluation, and governance, and prompt and tool design. Few enterprise IT teams have enough specialized AI experts to manage the rapidly increasing workload, leaving your existing developers overwhelmed and struggling to keep up.
Governance gaps
50% of executives cite translating responsible AI principles into operational progress as their top blocker.
If your IT team doesn’t have clear, defined guardrails on what AI agents are allowed to do, the odds of those agents acting erratically multiply. To make sure agents align with enterprise goals, leaders must govern agents consistently across the enterprise. They need a framework that includes strict access controls, real-time monitoring, and human-in-the-loop guidelines.
Trust
37% of leaders rank accuracy and fairness of AI outputs as a top society-wide AI challenge between now and 2030, second only to cybersecurity and misinformation (38%).
This challenge reflects less of a crisis of trust and more open questions about how reliably agents reason, how their actions are justified, and where agents should be applied within existing systems and workflows. Once agents start acting autonomously, portfolio-wide visibility becomes non-negotiable. IT leaders need explainability to understand how agents make decisions and establish clear points where humans can step in when needed. Keeping decision logs and summaries can help teams overcome a lack of trust in AI recommendations.
Aligning to enterprise goals
95% of AI pilots fail to deliver ROI.
Many AI pilots focus too much on flash and not enough on substance. If pilots aren’t directly linked to business goals, you’ll never be able to prove they’re worth the investment. A better approach is to prioritize use cases that can move profit-and-loss metrics, such as cycle time, cost to serve, or error rate.
Lessons from leaders who scale agentic AI
Despite the enormous hurdles, some global IT leaders are already building the agentic enterprise of the future, achieving real-world gains by automating core business processes. The most successful companies start by laying a unified technical foundation built upon these three principles.
1. Unified data access
AI agents need unified data to reason as humans do. With a data fabric or foundational data layer, agents gain a single, trusted view of information across the enterprise, improving accuracy and building trust.
2. Cloud-native architecture
As teams grow more comfortable with AI doing the work, they’ll expect agents to complete increasingly complex tasks with more than just a few steps. Cloud-native environments support this growth, allowing teams to scale agents as demand grows, iterate faster, and improve agent behavior over time.
3. A single platform for agent development
Agents built with disparate, one-off tools currently limit data integration and can compromise security. By contrast, with a single AI development platform, teams can build, customize, scale, and even orchestrate AI agents in one place, across the full development lifecycle. Therefore, a single platform that can manage custom apps and agents together is an excellent option.
From pilot to production: Avoiding the dreaded drop-off point
Most agentic AI efforts collapse right at the point where they should be scaling. They were built with tools that did not take into account all the work that goes into moving one into production. To avoid this, leaders should focus on a few practical pivots that separate stalled pilots from successful programs.
Simplify agent development with a unified AI development platform
AI agents will need constant updating. As pilots multiply, deficiencies compound, and changes to one agent can secretly break two others. With disparate tools, leaders lack visibility into agents, lineage, and their interactions with data and workflows.
Having one flexible platform to manage and govern the full lifecycle of agents solves the problem. A flexible, easy-to-use AI development platform offers a visual UI, prompts, and reusable components, enabling teams to iterate quickly in response to new data, business rules, policies, and edge cases. They also provide end-to-end security within the context of an enterprise’s entire app portfolio.
Using an AI development platform, teams can:
- Avoid technical debt.
- Orchestrate multi-agent workflows with a simple drag-and-drop interface.
- Turn complex tasks into visual, modern processes.
- Translate AI into real-world outcomes.
Build an AI-ready culture with humans still in the loop
Building a culture of AI starts with people. Employees must understand how AI agents will impact their day-to-day work so teams can uncover where agents will bring the most value.
While agents will work mostly autonomously, humans will need to intervene to make sure agents behave properly and deliver tangible results. That’s where a human-in-the-loop architecture brings benefits. With a platform approach to agentic AI, teams can decide when human intervention is needed, such as when agents process sensitive data or when their actions carry legal or financial implications, and that’s when real AI ROI happens.
Make data, security, and observability your top priorities
As you deploy more AI agents, IT teams must keep total control and fully understand how and why agents make their decisions. To do so, leaders should choose agentic AI platforms that provide a single command center where teams apply consistent guardrails and follow the agent’s reasoning and actions. When agents behave predictably, teams and stakeholders gain confidence and trust in agents.
In addition, continuous, real-time monitoring lets teams view agent behavior during development or runtime and track reasoning, response quality, and costs. With this level of visibility, teams can understand which agent initiated a step, which tools or APIs it called, and what data it used to make a decision. Detailed audit trails help teams find and fix issues before they turn into bigger problems.
And because agents will access sensitive data, your agentic platform enables you to enforce access control, governance, and AI usage limits so agents act predictably and data stays secure.
Prioritize the right use cases (and measure the right outcomes)
Teams should let KPIs lead agentic AI deployment. Identify metrics up front to track performance and move toward measurable results. Seek opportunities to automate repetitive, decision-heavy processes that create bottlenecks or cost the enterprise time or money.
Also, choose partners that align with your agentic AI strategy. Prioritize vendors with an AI development platform that supports the full AI agent lifecycle, with enterprise-grade security, scalability, and governance built in, as well as seamless integration with diverse data sources and custom AI models.
Build the agentic enterprise with the OutSystems AI development platform
The OutSystems AI development platform makes becoming an agentic enterprise easier. OutSystems empowers you and your IT team to use agents and applications to improve customer experience, employee productivity, and operational effectiveness. Your solutions aren’t just isolated “vibe-coding” experiments, but production-ready agentic systems grounded in your full enterprise context and integrated with your existing business.
Your applications, channels, and APIs evolve to be deeply integrated with agents. And you can do it all in one unified place for building, managing, and governing portfolios of integrated apps and agents while orchestrating other systems, data sources, models, and third-party agents. Built for agility, the platform also provides fully integrated DevOps with one-click publishing, to automate your path to production while eliminating the manual "glue code" that stalls most AI projects.
Fast-track agentic AI innovation
The OutSystems AI development platform can deliver the trusted, high-velocity AI innovation that leads to agentic AI projects that thrive in production. That’s why OutSystems is trusted by thousands of organizations worldwide. Supported by flexible runtime deployment options, built-in reliability, resiliency, and scalability, and expert round-the-clock support, you can put AI to work where it matters.
To learn more about the practical application of agentic AI for IT leaders:
